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pacman::p_load(sf, tidyverse, funModeling, tmap)February 13, 2023
Packages are important. Try to use SF, not SP
geoNGA <- st_read("data/geospatial/nigeria_nga_l2/", layer="geoBoundaries-NGA-ADM2") %>%
st_transform(crs=26392)Reading layer `geoBoundaries-NGA-ADM2' from data source
`D:\Users\Wen Yang\Documents\wyt05\IS415-GAA-WY\lessons\lesson2\data\geospatial\nigeria_nga_l2'
using driver `ESRI Shapefile'
Simple feature collection with 774 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84
The below file is the same as the above.
Except this has more information
NGA <- st_read("data/geospatial/nigeria_nga_l2/", layer="nga_admbnda_adm2_osgof_20190417") %>%
st_transform(crs=26392)Reading layer `nga_admbnda_adm2_osgof_20190417' from data source
`D:\Users\Wen Yang\Documents\wyt05\IS415-GAA-WY\lessons\lesson2\data\geospatial\nigeria_nga_l2'
using driver `ESRI Shapefile'
Simple feature collection with 774 features and 16 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS: WGS 84
We pick the second one as it gives the state and LGA (local government area) boundary
Convert water point data into sf point features
You can take the latitude degree and longitude degress
# A tibble: 95,008 × 71
row_id `#source` #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
<dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 429068 GRID3 7.98 5.12 08/29/… Unknown <NA> <NA> Tapsta…
2 222071 Federal Minis… 6.96 3.60 08/16/… Yes Boreho… Well Mechan…
3 160612 WaterAid 6.49 7.93 12/04/… Yes Boreho… Well Hand P…
4 160669 WaterAid 6.73 7.65 12/04/… Yes Boreho… Well <NA>
5 160642 WaterAid 6.78 7.66 12/04/… Yes Boreho… Well Hand P…
6 160628 WaterAid 6.96 7.78 12/04/… Yes Boreho… Well Hand P…
7 160632 WaterAid 7.02 7.84 12/04/… Yes Boreho… Well Hand P…
8 642747 Living Water … 7.33 8.98 10/03/… Yes Boreho… Well Mechan…
9 642456 Living Water … 7.17 9.11 10/03/… Yes Boreho… Well Hand P…
10 641347 Living Water … 7.20 9.22 03/28/… Yes Boreho… Well Hand P…
# … with 94,998 more rows, 62 more variables: `#water_tech_category` <chr>,
# `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
# `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
# `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
# `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
# `#pay` <chr>, `#fecal_coliform_presence` <chr>,
# `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …
Convert tibble dataframe into SF object
Need to convert Aspatial into Geospatial, but they do not have projection.
So need to tell R what is the projection in Aspatial (if it is wgs84, reconvert to that) -> then transform from 26392
Simple feature collection with 95008 features and 70 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 2.707441 ymin: 4.301812 xmax: 14.21828 ymax: 13.86568
Geodetic CRS: WGS 84
# A tibble: 95,008 × 71
row_id `#source` #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
* <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 429068 GRID3 7.98 5.12 08/29/… Unknown <NA> <NA> Tapsta…
2 222071 Federal Minis… 6.96 3.60 08/16/… Yes Boreho… Well Mechan…
3 160612 WaterAid 6.49 7.93 12/04/… Yes Boreho… Well Hand P…
4 160669 WaterAid 6.73 7.65 12/04/… Yes Boreho… Well <NA>
5 160642 WaterAid 6.78 7.66 12/04/… Yes Boreho… Well Hand P…
6 160628 WaterAid 6.96 7.78 12/04/… Yes Boreho… Well Hand P…
7 160632 WaterAid 7.02 7.84 12/04/… Yes Boreho… Well Hand P…
8 642747 Living Water … 7.33 8.98 10/03/… Yes Boreho… Well Mechan…
9 642456 Living Water … 7.17 9.11 10/03/… Yes Boreho… Well Hand P…
10 641347 Living Water … 7.20 9.22 03/28/… Yes Boreho… Well Hand P…
# … with 94,998 more rows, 62 more variables: `#water_tech_category` <chr>,
# `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
# `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
# `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
# `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
# `#pay` <chr>, `#fecal_coliform_presence` <chr>,
# `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …
Convert to the Nigeria projection system
Simple feature collection with 95008 features and 70 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 28907.91 ymin: 33736.93 xmax: 1293293 ymax: 1092883
Projected CRS: Minna / Nigeria Mid Belt
# A tibble: 95,008 × 71
row_id `#source` #lat_…¹ #lon_…² #repo…³ #stat…⁴ #wate…⁵ #wate…⁶ #wate…⁷
* <dbl> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <chr>
1 429068 GRID3 7.98 5.12 08/29/… Unknown <NA> <NA> Tapsta…
2 222071 Federal Minis… 6.96 3.60 08/16/… Yes Boreho… Well Mechan…
3 160612 WaterAid 6.49 7.93 12/04/… Yes Boreho… Well Hand P…
4 160669 WaterAid 6.73 7.65 12/04/… Yes Boreho… Well <NA>
5 160642 WaterAid 6.78 7.66 12/04/… Yes Boreho… Well Hand P…
6 160628 WaterAid 6.96 7.78 12/04/… Yes Boreho… Well Hand P…
7 160632 WaterAid 7.02 7.84 12/04/… Yes Boreho… Well Hand P…
8 642747 Living Water … 7.33 8.98 10/03/… Yes Boreho… Well Mechan…
9 642456 Living Water … 7.17 9.11 10/03/… Yes Boreho… Well Hand P…
10 641347 Living Water … 7.20 9.22 03/28/… Yes Boreho… Well Hand P…
# … with 94,998 more rows, 62 more variables: `#water_tech_category` <chr>,
# `#facility_type` <chr>, `#clean_country_name` <chr>, `#clean_adm1` <chr>,
# `#clean_adm2` <chr>, `#clean_adm3` <chr>, `#clean_adm4` <chr>,
# `#install_year` <dbl>, `#installer` <chr>, `#rehab_year` <lgl>,
# `#rehabilitator` <lgl>, `#management_clean` <chr>, `#status_clean` <chr>,
# `#pay` <chr>, `#fecal_coliform_presence` <chr>,
# `#fecal_coliform_value` <dbl>, `#subjective_quality` <chr>, …
The code below takes only the relevant column (column 3,4,8,9). As we only need them
Check for the quality of the data (data duplication, spatial data we want to check for missing value)
In this case, we have a lot of duplicated fields
[1] "Bassa" "Ifelodun" "Irepodun" "Nasarawa" "Obi" "Surulere"
The reason why is because there are 6 LGAs with the same name, but are in different states.
NGA$ADM2_EN[94] <- "Bassa, Kogi"
NGA$ADM2_EN[95] <- "Bassa, Plateau"
NGA$ADM2_EN[304] <- "Ifelodun, Kwara"
NGA$ADM2_EN[305] <- "Ifelodun, Osun"
NGA$ADM2_EN[355] <- "Irepodun, Kwara"
NGA$ADM2_EN[356] <- "Irepodun, Osun"
NGA$ADM2_EN[519] <- "Nasawara, Kano"
NGA$ADM2_EN[520] <- "Nasawara, Nasawara"
NGA$ADM2_EN[546] <- "Obi, Benue"
NGA$ADM2_EN[547] <- "Obi, Nasawara"
NGA$ADM2_EN[693] <- "Surulere, Lagos"
NGA$ADM2_EN[694] <- "Surulere, Oyo"
#status_clean frequency percentage cumulative_perc
1 Functional 45883 48.29 48.29
2 Non-Functional 29385 30.93 79.22
3 <NA> 10656 11.22 90.44
4 Functional but needs repair 4579 4.82 95.26
5 Non-Functional due to dry season 2403 2.53 97.79
6 Functional but not in use 1686 1.77 99.56
7 Abandoned/Decommissioned 234 0.25 99.81
8 Abandoned 175 0.18 99.99
9 Non functional due to dry season 7 0.01 100.00
Mutate allows us to do data processing, replacing #status_clean to remove the ‘#’
We replace all na fields with ‘unknown’
SF is a simple feature object, it will always have a geometric field, even though we only select one column which is ‘status_clean’
Functional
Non-functional
Unknown
The code below tells us how many water point intersects each LGA. (functional, nonfunctional, unknown) then append that information into the original NGA dataframe by creating a new dataframe NGA_wp
NGA_wp <- NGA %>%
mutate(`total_wp` = lengths(
st_intersects(NGA, wp_sf_nga))) %>%
mutate(`wp_functional` = lengths(
st_intersects(NGA, wp_functional))) %>%
mutate(`wp_nonfunctional` = lengths(
st_intersects(NGA, wp_nonfunctional))) %>%
mutate(`wp_unknown` = lengths(
st_intersects(NGA, wp_unknown)))ggplot(data = NGA_wp, aes(x = total_wp)) +
geom_histogram(bins=20, color="black", fill="light blue") +
geom_vline(aes(xintercept=mean(total_wp,na.rm=T)), color="red",linetype="dashed", size=0.8) +
ggtitle("Distribution of total water points by LGA") +
xlab("No. of water points") +
ylab("No. of\nLGAs") +
theme(axis.title.y=element_text(angle=0))